Empirical valence bond calculations of enzyme catalysis

Efficient and easible biocatalysts: Strategies for enzyme improvement. A review

Owing to the environmental friendliness and vast advantages that enzymes offer in the biotechnology and industry fields, biocatalysts are a prolific investigation field. However, the low catalytic activity, stability, and specific selectivity of the enzyme limit the range of the reaction enzymes involved in. A comprehensive understanding of the protein structure and dynamics in terms of molecular details enables us to tackle these limitations effectively and enhance the catalytic activity by enzyme engineering or modifying the supports and solvents. Along with different strategies including computational, enzyme engineering based on DNA recombination, enzyme immobilization, additives, chemical modification, and physicochemical modification approaches can be promising for the wide spread of industrial enzyme usage. This is attributed to the successful application of biocatalysts in industrial and synthetic processes requires a system that exhibits stability, activity, and reusability in a continuous flow process, thereby reducing the production cost. The main goal of this review is to display relevant approaches for improving enzyme characteristics to overcome their industrial application.

Subsystem density-functional theory: A reliable tool for spin-density based properties

Subsystem density-functional theory compiles a set of features that allow for efficiently calculating properties of very large open-shell radical systems such as organic radical crystals, proteins, or deoxyribonucleic acid stacks. It is computationally less costly than correlated ab initio wave function approaches and can pragmatically avoid the overdelocalization problem of Kohn-Sham density-functional theory without employing hard constraints on the electron-density. Additionally, subsystem density-functional theory calculations commonly start from isolated fragment electron densities, pragmatically preserving a priori specified subsystem spin-patterns throughout the calculation. Methods based on subsystem density-functional theory have seen a rapid development over the past years and have become important tools for describing open-shell properties. In this Perspective, we address open questions and possible developments toward challenging future applications in connection with subsystem density-functional theory for spin-dependent properties.

Mechanism Study of Proteins under Membrane Environment

Membrane proteins play crucial roles in various physiological processes, including molecule transport across membranes, cell communication, and signal transduction. Approximately 60% of known drug targets are membrane proteins. There is a significant need to deeply understand the working mechanism of membrane proteins in detail, which is a challenging work due to the lack of available membrane structures and their large spatial scale. Membrane proteins carry out vital physiological functions through conformational changes. In the current study, we utilized a coarse-grained (CG) model to investigate three representative membrane protein systems: the TMEM16A channel, the family C GPCRs mGlu2 receptor, and the P4-ATPase phospholipid transporter. We constructed the reaction pathway of conformational changes between the two-end structures. Energy profiles and energy barriers were calculated. These data could provide reasonable explanations for TMEM16A activation, the mGlu2 receptor activation process, and P4-ATPase phospholipid transport. Although they all belong to the members of membrane proteins, they behave differently in terms of energy. Our work investigated the working mechanism of membrane proteins and could give novel insights into other membrane protein systems of interest.

Application of Coarse-Grained (CG) Models to Explore Conformational Pathway of Large-Scale Protein Machines

Protein machines are clusters of protein assemblies that function in order to control the transfer of matter and energy in cells. For a specific protein machine, its working mechanisms are not only determined by the static crystal structures, but also related to the conformational transition dynamics and the corresponding energy profiles. With the rapid development of crystallographic techniques, the spatial scale of resolved structures is reaching up to thousands of residues, and the concomitant conformational changes become more and more complicated, posing a great challenge for computational biology research. Previously, a coarse-grained (CG) model aiming at conformational free energy evaluation was developed and showed excellent ability to reproduce the energy profiles by accurate electrostatic interaction calculations. In this study, we extended the application of the CG model to a series of large-scale protein machine systems. The spike protein trimer of SARS-CoV-2, ATP citrate lyase (ACLY) tetramer, and P4-ATPases systems were carefully studied and discussed as examples. It is indicated that the CG model is effective to depict the energy profiles of the conformational pathway between two endpoint structures, especially for large-scale systems. Both the energy change and energy barrier between endpoint structures provide reasonable mechanism explanations for the associated biological processes, including the opening of receptor binding domain (RBD) of spike protein, the phospholipid transportation of P4-ATPase, and the loop translocation of ACLY. Taken together, the CG model provides a suitable alternative in mechanistic studies related to conformational change in large-scale protein machines.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Multistate Reactive Molecular Dynamics Simulations of Proton Diffusion in Water Clusters and in the Bulk

The molecular mechanics with proton transfer (MMPT) force field is combined with multistate adiabatic reactive molecular dynamics (MS-ARMD) to describe proton transport in the condensed phase. Parametrization for small protonated water clusters based on electronic structure calculations at the MP2/6-311+G(2d,2p) level of theory and refinement by comparing with infrared spectra for a protonated water tetramer yields a force field which faithfully describes the minimum energy structures of small protonated water clusters. In protonated water clusters up to (H₂O)₁₀₀H⁺, the proton hopping rate is around 100 hops/ns. This rate converges for 21 ≤ n ≤ 31, and no further speedup in bulk water is found. This indicates that bulklike behavior requires the solvation of a Zundel motif by ∼25 water molecules, which corresponds to the second solvation sphere. For smaller cluster sizes, the number of available states (i.e., the number of proton acceptors) is too small and slows down proton-transfer rates. The cluster simulations confirm that the excess proton is typically located on the surface. The free-energy surface as a function of the weights of the two lowest states and a configurational parameter suggests that the "special pair" plays a central role in rapid proton transport. The barriers between this minimum-energy structure and the Zundel and Eigen minima are sufficiently low (∼1 kcal/mol, consistent with recent experiments and commensurate with a hopping rate of ∼100/ns or 1 every 10 ps), leading to a highly dynamic environment. These findings are also consistent with recent experiments which find that Zundel-type hydration geometries are prevalent in bulk water.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Engineering enzyme access tunnels

Enzymes are efficient and specific catalysts for many essential reactions in biotechnological and pharmaceutical industries. Many times, the natural enzymes do not display the catalytic efficiency, stability or specificity required for these industrial processes. The current enzyme engineering methods offer solutions to this problem, but they mainly target the buried active site where the chemical reaction takes place. Despite being many times ignored, the tunnels and channels connecting the environment with the active site are equally important for the catalytic properties of enzymes. Changes in the enzymatic tunnels and channels affect enzyme activity, specificity, promiscuity, enantioselectivity and stability. This review provides an overview of the emerging field of enzyme access tunnel engineering with case studies describing design of all the aforementioned properties. The software tools for the analysis of geometry and function of the enzymatic tunnels and channels and for the rational design of tunnel modifications will also be discussed. The combination of new software tools and enzyme engineering strategies will provide enzymes with access tunnels and channels specifically tailored for individual industrial processes.

Reactive Molecular Dynamics Study of Effects of Small-Molecule Organic Acids on PMIA Thermal Decomposition

Reactive molecular dynamics was used to investigate the atomic-level mechanism of formic acid-accelerated deterioration of meta-aramid (PMIA) fibers. The simulation results showed that formic acid promoted PMIA decomposition. The activation energy of a composite system (PF) consisting of formic acid and PMIA was 106.94 kJ/mol at 2000-3000 K, which is 11.95% lower than that of pure PMIA. The main small-molecule products of the PF system were H/C/O-containing molecules (H₂O, CO, and CO₂), hydrocarbon molecules (e.g., CH₄, ^•C₂H, C₂H₄, and C₃H₄), N-containing molecules (N₂, NH₃, and HCN), H₂, and various free radicals. Formic acid can promote the production of small molecules such as CO, CO₂, and H₂O. The N-H bonds, C-N bonds and the amide C═O double bond of PMIA were vulnerable to CO, H ions, and free radicals produced by formic acid decomposition, and this decreased the PMIA stability. Temperature is an important factor in the thermal decomposition of PMIA and can accelerate reactions in the PF system. The initial reaction rate of PMIA at 3000 K was 8.1 times that at 2000 K, and the intermediate reaction rate was 6.2 times that at 2200 K; temperature also affects the types of pyrolysis products, for example, hydrocarbons are high-temperature products.

Molecular Dynamics Simulations on the Thermal Decomposition of Meta-Aramid Fibers

The thermal decomposition mechanism of a meta-aramid fiber was simulated at the atomic level using the ReaxFF reactive force field. The simulation results indicated that the main initial decomposition positions of the meta-aramid fiber elements were Caromatic ring⁻N and C=O, which could be used as targets for the modification of meta-aramid fibers. The meta-aramid fiber elements first decomposed into C6⁻C13 and then into smaller segments and micromolecular gases. The temperature was shown to be the key factor affecting the thermal decomposition of the meta-aramid fibers. More complex compositions and stable gases were produced at high temperatures than at lower temperatures. HCN was a decomposition product at high temperature, suggesting that its presence could be used for detecting thermal faults in meta-aramid fibers. Generation path tracing of the thermal decomposition products NH₃ and H₂O was also performed. NH₃ was produced when the NH₂ group captured an H atom adjacent to the system. H₂O was formed after a carbonyl group captured an H atom, became a hydroxyl group, with subsequent intramolecular dehydration or intermolecular hydrogen abstraction.

Structural Articulation of Biochemical Reactions Using Restrained Geometries and Topology Switching

A strategy named "restrained geometries and topology switching" (RGATS) is presented to obtain detailed trajectories for complex biochemical reactions using molecular mechanics (MM) methods. It enables prediction of realistic dynamical pathways for chemical reactions, especially for accurately characterizing the structural adjustments of highly complex environments to any proximal biochemical reaction. It can be used to generate reactive conformations, model stepwise or concerted reactions in complex environments, and probe the influence of changes in the environment. Its ability to take reactively nonoptimal conformations and generate favorable starting conformations for a biochemical reaction is illustrated for a proton transfer between two model compounds. Its ability to study concerted reactions in explicit solvent is illustrated using proton transfers between an ammonium ion and two conserved histidines in an ammonia transporter channel embedded in a lipid membrane. Its ability to characterize the changes induced by subtle differences in the active site environment is illustrated using nucleotide addition by a DNA polymerase in the presence of two versus three Mg²⁺ ions. RGATS can be employed within any MM program and requires no additional software implementation. This allows the full assortment of computational methods implemented in all available MM programs to be used to tackle virtually any question about biochemical reactions that is answerable without using a quantum mechanical (QM) model. It can also be applied to generate reasonable starting structures for more detailed and expensive QM or QM/MM methods. In particular, this strategy enables rapid prediction of reactant, intermediary, or product state structures in any macromolecular context, with the only requirement being that the structure in any one of these states is either known or can be accurately modeled.

Theoretical estimation of redox potential of biological quinone cofactors

Redox potentials are essential to understand biological cofactor reactivity and to predict their behavior in biological media. Experimental determination of redox potential in biological system is often difficult due to complexity of biological media but computational approaches can be used to estimate them. Nevertheless, the quality of the computational methodology remains a key issue to validate the results. Instead of looking to the best absolute results, we present here the calibration of theoretical redox potential for quinone derivatives in water coupling QM + MM or QM/MM scheme. Our approach allows using low computational cost theoretical level, ideal for long simulations in biological systems, and determination of the uncertainties linked to the calculations. © 2017 Wiley Periodicals, Inc.

Empirical Valence Bond Simulations of the Hydride-Transfer Step in the Monoamine Oxidase A Catalyzed Metabolism of Noradrenaline

Monoamine oxidases (MAOs) A and B are flavoenzymes responsible for the metabolism of biogenic amines, such as dopamine, serotonin, and noradrenaline (NA), which is why they have been extensively implicated in the etiology and course of various neurodegenerative disorders and, accordingly, used as primary pharmacological targets to treat these debilitating cognitive diseases. The precise chemical mechanism through which MAOs regulate the amine concentration, which is vital for the development of novel inhibitors, is still not unambiguously determined in the literature. In this work, we present atomistic empirical valence bond simulations of the rate-limiting step of the MAO-A-catalyzed NA (norepinephrine) degradation, involving hydride transfer from the substrate α-methylene group to the flavin moiety of the flavin adenine dinucleotide prosthetic group, employing the full dimensionality and thermal fluctuations of the hydrated enzyme, with extensive configurational sampling. We show that MAO-A lowers the free energy of activation by 14.3 kcal mol^-1 relative to that of the same reaction in aqueous solution, whereas the calculated activation free energy of ΔG^‡ = 20.3 ± 1.6 kcal mol^-1 is found to be in reasonable agreement with the correlated experimental value of 16.5 kcal mol^-1. The results presented here strongly support the fact that both MAO-A and MAO-B isoforms function by the same hydride-transfer mechanism. We also considered a few point mutations of the "aromatic cage" tyrosine residue (Tyr444Phe, Tyr444Leu, Tyr444Trp, Tyr444His, and Tyr444Glu), and the calculated changes in the reaction barriers are in agreement with the experimental values, thus providing further support to the proposed mechanism.

Empirical valence bond simulations of the hydride transfer step in the monoamine oxidase B catalyzed metabolism of dopamine

Monoamine oxidases (MAOs) A and B are flavoenzymes responsible for the metabolism of biogenic amines such as dopamine, serotonin and noradrenaline. In this work, we present a comprehensive study of the rate-limiting step of dopamine degradation by MAO B, which consists in the hydride transfer from the methylene group of the substrate to the flavin moiety of the FAD prosthetic group. This article builds on our previous quantum chemical study of the same reaction using a cluster model (Vianello et al., Eur J Org Chem 2012; 7057), but now considering the full dimensionality of the hydrated enzyme with extensive configurational sampling. We show that MAO B is specifically tuned to catalyze the hydride transfer step from the substrate to the flavin moiety of the FAD prosthetic group and that it lowers the activation barrier by 12.3 kcal mol⁻¹ compared to the same reaction in aqueous solution, a rate enhancement of more than nine orders of magnitude. Taking into account the deprotonation of the substrate prior to the hydride transfer reaction, the activation barrier in the enzyme is calculated to be 16.1 kcal mol⁻¹, in excellent agreement with the experimental value of 16.5 kcal mol⁻¹. Additionally, we demonstrate that the protonation state of the active site residue Lys296 does not have an influence on the hydride transfer reaction.

Comparison of ab Initio, DFT, and Semiempirical QM/MM Approaches for Description of Catalytic Mechanism of Hairpin Ribozyme

We have analyzed the capability of state-of-the-art multiscale computational approaches to provide atomic-resolution electronic structure insights into possible catalytic scenarios of the hairpin ribozyme by evaluating potential and free energy surfaces of the reactions by various hybrid QM/MM methods. The hairpin ribozyme is a unique catalytic RNA that achieves rate acceleration similar to other small self-cleaving ribozymes but without direct metal ion participation. Guanine 8 (G8) and adenine 38 (A38) have been identified as the catalytically essential nucleobases. However, their exact catalytic roles are still being investigated. In line with the available experimental data, we considered two reaction scenarios involving protonated A38H(+) as a general acid which is further assisted by either canonical G8 or deprotonated G8(-) forms. We used the spin-component scaled Møller-Plesset (SCS-MP2) method at the complete basis set limit as the reference method. The semiempirical AM1/d-PhoT and SCC-DFTBPR methods provided acceptable activation barriers with respect to the SCS-MP2 data but predicted significantly different reaction pathways. DFT functionals (BLYP and MPW1K) yielded the same reaction pathway as the SCS-MP2 method. The activation barriers were slightly underestimated by the GGA BLYP functional, although with accuracy comparable to the semiempirical methods. The SCS-MP2 method and hybrid MPW1K functional gave activation barriers that were closest to those derived from experimentally measured rate constants.

A quantitative measure of electrostatic perturbation in holo and apo enzymes induced by structural changes

Biological pathways are subject to subtle manipulations that achieve a wide range of functional variation in differing physiological niches. In many instances, changes in the structure of an enzyme on ligand binding germinate electrostatic perturbations that form the basis of its changed catalytic or transcriptional efficiency. Computational methods that seek to gain insights into the electrostatic changes in enzymes require expertise to setup and computing prowess. In the current work, we present a fast, easy and reliable methodology to compute electrostatic perturbations induced by ligand binding (MEPP). The theoretical foundation of MEPP is the conserved electrostatic potential difference (EPD) in cognate pairs of active site residues in proteins with the same functionality. Previously, this invariance has been used to unravel promiscuous serine protease and metallo-β-lactamase scaffolds in alkaline phosphatases. Given that a similarity in EPD is significant, we expect differences in the EPD to be significant too. MEPP identifies residues or domains that undergo significant electrostatic perturbations, and also enumerates residue pairs that undergo significant polarity change. The gain in a certain polarity of a residue with respect to neighboring residues, or the reversal of polarity between two residues might indicate a change in the preferred ligand. The methodology of MEPP has been demonstrated on several enzymes that employ varying mechanisms to perform their roles. For example, we have attributed the change in polarity in residue pairs to be responsible for the loss of metal ion binding in fructose 1,6-bisphosphatases, and corroborated the pre-organized state of the active site of the enzyme with respect to functionally relevant changes in electric fields in ketosteroid isomerases.

Optimization of reorganization energy drives evolution of the designed Kemp eliminase KE07

Understanding enzymatic evolution is essential to engineer enzymes with improved activities or to generate enzymes with tailor-made activities. The computationally designed Kemp eliminase KE07 carries out an unnatural reaction by converting of 5-nitrobenzisoxazole to cyanophenol, but its catalytic efficiency is significantly lower than those of natural enzymes. Three series of designed Kemp eliminases (KE07, KE70, KE59) were shown to be evolvable with considerable improvement in catalytic efficiency. Here we use the KE07 enzyme as a model system to reveal those forces, which govern enzymatic evolution and elucidate the key factors for improving activity. We applied the Empirical Valence Bond (EVB) method to construct the free energy pathway of the reaction in the original KE07 design and the evolved R7 1/3H variant. We analyzed catalytic effect of residues and demonstrated that not all mutations in evolution are favorable for activity. In contrast to the small decrease in the activation barrier, in vitro evolution significantly reduced the reorganization energy. We developed an algorithm to evaluate group contributions to the reorganization energy and used this approach to screen for KE07 variants with potential for improvement. We aimed to identify those mutations that facilitate enzymatic evolution, but might not directly increase catalytic efficiency. Computational results in accord with experimental data show that all mutations, which appear during in vitro evolution were either neutral or favorable for the reorganization energy. These results underscore that distant mutations can also play role in optimizing efficiency via their contribution to the reorganization energy. Exploiting this principle could be a promising strategy for computer-aided enzyme design. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.

The energy gap as a universal reaction coordinate for the simulation of chemical reactions

The selection of a proper reaction coordinate is a major bottleneck in simulations of chemical reactions in complex systems. Increasing the number of variables that are used to bias the reaction largely affects the convergence and leads to an unbearable increase in computational price. This problem can be overcome by employing a complex reaction coordinate that depends on many geometrical variables of the system, such as the energy gap (EGAP) in the empirical valence bond (EVB) method. EGAP depends on all of the coordinates of the system, and its robustness has been demonstrated for a variety of enzymatic reactions. In this work, we demonstrate that EGAP, derived from a classical representation, can be used as a reaction coordinate in systems described with any quantum chemistry Hamiltonian. Benefits of using EGAP as a reaction coordinate as compared to a traditional geometrical variable are illustrated in the case of a symmetric nucleophilic substitution reaction in water solution. EGAP is shown to provide a significantly more efficient sampling and allows a better localization of the transition state as compared to a geometrical reaction coordinate.

Quantum mechanical methods for enzyme kinetics

This review discusses methods for the incorporation of quantum mechanical effects into enzyme kinetics simulations in which the enzyme is an explicit part of the model. We emphasize three aspects: (a) use of quantum mechanical electronic structure methods such as molecular orbital theory and density functional theory, usually in conjunction with molecular mechanics; (b) treating vibrational motions quantum mechanically, either in an instantaneous harmonic approximation, or by path integrals, or by a three-dimensional wave function coupled to classical nuclear motion; (c) incorporation of multidimensional tunneling approximations into reaction rate calculations.

The design of biologically active polypeptides

On the one hand considerable advances are being made in the computer-aided calculation of polypeptide structure and behaviour, while on the other even very simple reasoning has lead to some apparent successes in the design and synthesis of biologically active peptides. While this looks extremely promising, there is a case for arguing that progress made in both these areas is to some degree illusory. Such criticisms as can be made, however, turn out to be highly constructive, suggesting a profitable and still workable approach to the problem of rational polypeptide design.